| Description | GeneRatio | BgRatio | pvalue | p.adjust | qvalue | geneID | Count | group | |
| ID | |||||||||
| GO:0002455 | humoral immune response mediated by circulatin... | 22/178 | 150/18670 | 19.365993 | 16.222197 | 16.298589 | HLA-DQB1/CD55/IGHM/PTPRC/TRBC2/IGHG2/IGKV3-20/... | 22 | B6_up |
| GO:0006958 | complement activation, classical pathway | 20/178 | 137/18670 | 17.588789 | 14.989062 | 15.065454 | CD55/IGHM/TRBC2/IGHG2/IGKV3-20/IGHV4-34/IGHV3-... | 20 | B6_up |
| GO:0006956 | complement activation | 20/178 | 175/18670 | 15.453684 | 13.008859 | 13.085251 | CD55/IGHM/TRBC2/IGHG2/IGKV3-20/IGHV4-34/IGHV3-... | 20 | B6_up |
| GO:0038096 | Fc-gamma receptor signaling pathway involved i... | 18/178 | 139/18670 | 14.916693 | 12.675988 | 12.752379 | PTPRC/LYN/IGHG2/IGKV3-20/IGHV4-34/IGHV3-30/IGL... | 18 | B6_up |
| GO:0002673 | regulation of acute inflammatory response | 18/178 | 159/18670 | 13.871614 | 11.817674 | 11.894066 | HLA-E/CD55/IGHG2/IGKV3-20/IGHV4-34/IGHV3-30/IG... | 18 | B6_up |
首先我们可以借助 DotPlot的类方法parse_from_tidy_data 对数据进行封装，然后直接调用plot函数进行绘图。当然，你也可以通过DotPlot的构造函数__init__()来实例化DotPlot对象。
new_keys = {'item_key': 'Description','group_key': 'group','sizes_key': 'Count'}
dp = dotplot.DotPlot.parse_from_tidy_data(data, **new_keys)
sct = dp.plot(size_factor=10, cmap='Reds') # 通过size_factor 调节图中点的大小
dp = dotplot.DotPlot.parse_from_tidy_data(data, item_key='Description', group_key='group', sizes_key='Count') # 该效果完全同上，这是python语言特性 sct = dp.plot(size_factor=10, cmap='Reds')
我们可以通过color_key指定data中的列做颜色映射。
new_keys = {'item_key': 'Description','group_key': 'group','sizes_key': 'Count','color_key': 'pvalue'}
dp = dotplot.DotPlot.parse_from_tidy_data(data, **new_keys)
sct = dp.plot(size_factor=10, cmap='Reds', cluster_row=True)
可以通过circle_key增加一列作为虚线圆圈的映射。
DEFAULT_CLUSTERPROFILE_KEYS = {
'item_key': 'Description', 'group_key': 'group',
'sizes_key': 'Count', 'color_key': 'pvalue',
'circle_key': 'qvalue'
}
dp = dotplot.DotPlot.parse_from_tidy_data(data, **DEFAULT_CLUSTERPROFILE_KEYS)
sct = dp.plot(size_factor=10, cmap='Reds', cluster_row=True)
当然，更多的参数我们可以通过signature来查看，我对这些参数都做了类型注释，应该是通俗易懂的：
?dp.plot
Signature:
dp.plot(
size_factor:float=15,
vmin:float=0,
vmax:float=None,
path:Union[os.PathLike, NoneType]=None,
cmap:Union[str, matplotlib.colors.Colormap]='Reds',
cluster_row:bool=False,
cluster_col:bool=False,
cluster_kws:Union[Dict, NoneType]=None,
**kwargs,
)
Docstring:
:param size_factor: `size factor` * `value` for the actually representation of scatter size in the final figure
:param vmin: `vmin` in `matplotlib.pyplot.scatter`
:param vmax: `vmax` in `matplotlib.pyplot.scatter`
:param path: path to save the figure
:param cmap: color map supported by matplotlib
:param kwargs: dot_title, circle_title, colorbar_title, dot_color, circle_color
other kwargs are passed to `matplotlib.Axes.scatter`
:param cluster_row, whether to cluster the row
:param cluster_col, whether to cluster the col
:param cluster_kws, key args for cluster, including `cluster_method`, `cluster_metric`， 'cluster_n'
:return:
因此，我们可以通过关键字参数修改图例中的部分组件：
sct = dp.plot(size_factor=10, cmap='Reds', cluster_row=True, dot_title = 'Count', circle_title='-log10(qvalue)', colorbar_title = '-log10(pvalue)')
dotplot在数据可视化中是一个强有力的展示方式，选择一个合适的可视化方式胜过千言万语
最后，最适合的可视化方式是最直观、最简洁的，不是炫技，别被花里胡哨的可视化所迷住双眼而忽略了信息的传达。
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